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Assess the AI model and the vendor relationship as one risk. Start by defining the exact use, affected customers, data, integrations and consequences of failure. Then gather activity-specific evidence about the supplier, evaluate the model’s design and performance, record information gaps, contract for oversight, and monitor through a tested exit plan. Outsourcing the technology does not outsource the institution’s responsibility for safe, lawful operations.
What a third-party AI assessment must establish
A useful assessment answers two connected questions: Can this model perform the proposed task safely and reliably? and Can this supplier operate, secure, support and change the service under the institution’s legal and risk requirements? A technically impressive model can still be unsuitable if the provider cannot explain material limitations, protect data, maintain service continuity or cooperate with oversight.
U.S. banking agencies state that using a third party does not reduce a bank’s responsibility to operate safely and comply with applicable laws. The Federal Reserve’s Interagency Guidance on Third-Party Relationships also warns that prior familiarity with a supplier is not a substitute for diligence tailored to the specific activity. Treat the assessment as a documented risk decision, not as a procurement form or a claim that a vendor is “approved” for every use.
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1. Define the proposed use and its consequences
Do not begin with a generic AI questionnaire. Write a short use-case statement that makes the proposed activity testable. The statement should identify what the system will do, what it will not do, and who remains accountable for each decision.
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Record the use and decision context
- Business purpose: describe the workflow and the expected benefit, such as document classification, fraud-alert triage, customer-service assistance or analyst drafting.
- Decision influence: state whether the output is informational, recommends an action, ranks cases, triggers a control or directly determines an outcome. Identify any credit, underwriting, pricing, servicing, fraud, collections, employment or regulatory-reporting effect.
- Users and affected people: list employees, contractors, customers, applicants and other groups who may rely on or be affected by outputs.
- Human involvement: specify who reviews an output, what evidence they must consider, when they can override it and how overrides are recorded. “Human in the loop” is not meaningful unless the reviewer has enough time, information and authority to disagree.
- Data: inventory personal, financial, confidential, regulated and proprietary information; identify data residency, retention, training use and deletion expectations.
- Integrations: map APIs, identity systems, core banking platforms, case-management tools, external data feeds, plugins and subcontractors. A model that is low risk in a sandbox may become high risk when connected to production systems.
- Scale and operating conditions: estimate transaction volume, latency requirements, peak periods, geographic coverage, languages, expected prompts or features and fallback arrangements.
Classify impact and criticality
Describe plausible failure consequences, not only the intended benefit. Consider financial loss, discriminatory or inaccurate decisions, privacy exposure, misleading customer communications, operational disruption, legal violations, cyber abuse and reputational damage. Decide whether the service supports a critical or otherwise high-risk activity, whether a failure could affect many customers at once, and how quickly the institution could switch to a safe manual or alternate process.
This classification determines the depth of evidence, approval level, testing frequency, contract protections and monitoring. The interagency guidance calls for treatment proportionate to risk and complexity; copying the same checklist for a low-impact internal search tool and a customer-facing credit workflow defeats that principle.
2. Perform activity-specific vendor due diligence
Evaluate the supplier’s ability to perform the particular service and meet the institution’s legal, policy, security and safety expectations. A well-known company, a long relationship or a polished sales demonstration is not evidence that the proposed activity is controlled.
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Corporate, legal and financial capacity
- Confirm the legal entity, ownership, governing jurisdictions and authority to provide the service in each relevant location.
- Review regulatory or compliance history, litigation or enforcement issues relevant to the service, and the supplier’s process for notifying customers of material developments.
- Assess financial stability, funding, insurance, revenue concentration and the ability to sustain the service and support obligations during stress.
- Identify executives, technical owners, security leaders and key-person dependencies. Ask how duties are segregated between development, deployment, access administration, security and independent review.
- Examine staffing levels, specialist capability, training, turnover and access to personnel who can investigate model failures or security incidents.
Governance and independent controls
- Obtain the supplier’s governance structure for model approval, risk acceptance, release management, incident response and customer communication.
- Review internal audit, independent testing, penetration testing, vulnerability management and remediation tracking. Establish whether findings cover the service you will use or only a different environment.
- Examine security architecture, identity and privileged-access controls, encryption, key management, logging, secure development and data-loss prevention.
- Map all material subcontractors and fourth parties, including hosting, data, model, support and content providers. Determine how the supplier evaluates and monitors them.
- Review business-continuity, disaster-recovery and resilience testing, including recovery objectives, dependency failures, regional outages and communications during an incident.
Interpret certifications and reports correctly
SOC reports, ISO certificates and similar attestations can provide useful evidence about defined controls during a stated period. Check the scope, system description, exceptions, complementary user controls, testing dates and auditor opinion. A report or certificate does not prove that the specific AI service is accurate, fair, secure for your data or suitable for your intended decision. Require evidence that closes the gap between the attested environment and your proposed use.
Questions specific to an AI service
Ask the supplier to explain, in language your model-risk, compliance and technology teams can test:
- What model or models produce the output, and which components are proprietary, open source, fine-tuned or supplied by another party?
- What data is used for development, fine-tuning, retrieval, evaluation and ongoing improvement? Is customer content retained or used to train a shared model?
- What safeguards address prompt injection, data leakage, unsafe or biased outputs, unauthorized actions, fabricated citations and abusive use?
- What reliability measures exist for accuracy, availability, latency, rate limits, refusal behavior and degraded operation?
- How are model, data, system-prompt, retrieval-index and policy changes approved, tested, communicated and rolled back?
- What incident history, material outages, security events, performance regressions and customer complaints can be disclosed?
For credit unions, the National Credit Union Administration’s AI resource specifically identifies due diligence on how an AI product works, the risks it introduces, its fit with the credit union’s business model, and the safeguards and reliability measures in place.
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3. Assess the model and the evidence behind it
Model assessment should be matched to the proposed use. For models within the scope of the revised interagency model-risk guidance, review conceptual soundness, design, development data and performance; independently test and validate the model; analyze outcomes; and monitor whether it remains accurate, reliable and fit for purpose.
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| Evidence area | What to request | How to use it |
|---|---|---|
| Design and purpose | Technical description, intended population, operating conditions, inputs, outputs, decision thresholds and known exclusions | Check that the proposed use matches what the model was designed and evaluated to do. |
| Data | Development and evaluation-data descriptions, labeling approach, provenance, representativeness, retention and update practices | Identify coverage gaps, privacy constraints, stale data and risks of data or concept shift. |
| Performance | Evaluation methodology, metrics, confidence intervals where available, error analysis and results by relevant segment or operating condition | Determine whether aggregate performance hides unacceptable errors for a customer group, product, language or edge case. |
| Limitations | Failure modes, unsupported inputs, hallucination or uncertainty behavior, safety restrictions and conditions under which outputs must not be used | Turn limitations into use restrictions, human-review rules and monitoring thresholds. |
| Change history | Model, data, system-prompt, retrieval and infrastructure versions; release dates; validation results; rollback method | Decide which changes require notice, revalidation or approval before production use. |
| Incidents | Material outages, security events, harmful outputs, complaints, corrective actions and recurrence prevention | Test whether the supplier detects, reports and learns from failures. |
Ask for evidence proportionate to the risk; the agencies do not prescribe one universal test suite. A low-impact drafting assistant may need documented output sampling and access controls, while a model influencing credit or fraud decisions generally warrants stronger independent validation, segment analysis, outcome testing and ongoing monitoring.
Validate locally, not only in the vendor’s environment
Reproduce representative conditions with approved data or carefully controlled test data. Test normal cases, boundary cases, adversarial inputs, missing or conflicting information, language and accessibility needs, and the exact integrations and prompts used in production. Compare outputs with a defined reference process, document reviewer disagreement and measure both false positives and false negatives where applicable.
Analyze outcomes for relevant customer and operating segments. Investigate whether a model’s errors, refusal rates, confidence scores or response quality differ materially by product, geography, language, channel or customer characteristic. Set acceptance thresholds before reviewing results so that a favorable demonstration does not silently redefine success.
Address proprietary components
Proprietary code, weights, data or infrastructure can limit direct inspection and make validation harder. That limitation does not remove model-risk management responsibilities. Record what cannot be independently verified, why the missing evidence matters, what alternative evidence is available, and which controls compensate for the uncertainty. Possible compensating measures include narrower use, stronger human review, output filtering, additional sampling, lower transaction limits, independent testing or a different provider.
4. Handle information gaps as explicit risk decisions
When a supplier refuses, cannot or will not provide requested information, do not mark the item “not applicable” without explanation. Maintain an evidence-gap register with four fields:
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- Missing item: state precisely what was not supplied, such as segment-level error results, subcontractor identity or deletion confirmation.
- Why it matters: connect the gap to a decision, legal obligation, customer harm, security exposure or inability to validate.
- Residual confidence: record what can and cannot be concluded, who accepted the uncertainty and the date of that decision.
- Response: choose alternative evidence, an independent assessment, added controls, restricted deployment, delayed approval or another supplier.
Marketing claims, benchmark figures without methodology and general reputation are not substitutes for evidence about your use. The interagency guidance states that prior experience alone is not an adequate proxy for activity-specific diligence and recommends documenting limitations, mitigation and the possibility of selecting a different provider.
5. Contract for oversight, not just uptime
Commercial terms should make the control framework enforceable. Tailor the detail to risk and complexity, and have qualified counsel confirm jurisdiction-specific requirements.
| Contract topic | Terms to address |
|---|---|
| Service and performance | Defined functions, service levels, support response, accuracy or quality measures where measurable, maintenance windows and remedies. |
| Data handling | Permitted data, processing purpose, training restrictions, retention, deletion, location, encryption, access, return and secure disposal. |
| Security and privacy | Control requirements, vulnerability disclosure, testing cooperation, logging, access reviews, breach responsibilities and evidence delivery. |
| Incidents | Notice deadlines, required content, investigation cooperation, containment, customer and regulator support, root-cause reporting and corrective actions. |
| Model and service changes | Advance notice for material model, data, prompt, hosting, subcontractor or policy changes; testing information; approval, rollback and termination rights. |
| Audit and regulatory access | Access to relevant records, personnel and control evidence, including support for applicable regulatory examination. Define how remote or independent assessments work when direct access is limited. |
| Continuity and exit | Recovery commitments, transition assistance, data and configuration portability, intellectual-property treatment, migration support, deletion certification and survival of critical obligations. |
Do not promise a regulator that a contract automatically grants unrestricted access if the actual clause is narrower. Confirm the final language and enforceability for each jurisdiction and relationship.
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6. Monitor the relationship throughout its lifecycle
Approval is the start of oversight. Build a monitoring plan that tests both service performance and control effectiveness, with more frequent or deeper review for higher-risk uses and when conditions change.
Track operational and model signals
- Availability, latency, capacity, error rates, queue backlogs and recovery performance.
- Output quality, accuracy, drift, refusal behavior, harmful-output events, reviewer overrides and customer complaints.
- Performance by material segment, channel, product, geography and operating condition.
- Security alerts, access anomalies, data-loss events, vulnerabilities and unresolved audit findings.
- Model, data, system-prompt, retrieval, infrastructure and subcontractor changes, including whether required notice arrived on time.
- Supplier financial health, staffing, ownership, insurance, litigation, concentration and dependency indicators.
Define escalation and remediation
Set thresholds that trigger investigation, temporary restrictions, additional sampling, senior risk acceptance, customer notification, suspension or termination. Assign owners and due dates for remediation. A dashboard without an escalation path merely records a problem; it does not control it.
Keep an exit path workable
Before production, document how the institution would retrieve data, prompts, configurations, logs and intellectual property; revoke access; preserve records; notify affected customers; and move the activity to an alternate provider, manual process or internal system. Test the plan for a realistic outage or termination scenario. Consider portability, proprietary formats, undocumented dependencies, customer communications, minimum transition staffing and the time required to validate a replacement.
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How to compare providers that remain viable
Use a documented, institution-specific scoring or threshold method rather than implying that the agencies prescribe a universal rating scale. Compare providers on the same intended use and record trade-offs, exceptions and residual risk.
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|---|---|
| Intended-use fit | Performance in your workflow, population, languages, volumes, integrations and human-review design. |
| Validation transparency | Access to technical documentation, evaluation methods, segment results, limitations, version history and independent testing. |
| Privacy and security | Data-use terms, retention, isolation, encryption, identity controls, logging, incident history and control-test scope. |
| Legal and compliance support | Regulatory cooperation, records access, jurisdiction coverage, complaint handling and evidence delivery. |
| Financial and staffing capacity | Ability to sustain service, support, security response and remediation over the contract term. |
| Resilience and recovery | Redundancy, recovery objectives, test results, regional dependencies and practical fallback options. |
| Subcontractor and concentration risk | Fourth-party map, change notice, shared infrastructure, geographic concentration and substitutability. |
| Change and incident management | Advance notice, release controls, rollback, incident timelines and cooperation obligations. |
| Audit and monitoring rights | Access to reports, testing, logs, personnel and remediation evidence appropriate to the risk. |
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What the 2026 U.S. model-risk guidance does—and does not—cover
The Federal Reserve, Office of the Comptroller of the Currency and FDIC issued revised model-risk guidance on April 17, 2026. It is supervisory guidance, not an enforceable or prescriptive standard, and is tailored to an institution’s model-risk profile, size and complexity. It is expected to be most relevant to banking organizations above $30 billion in assets, while potentially remaining relevant to smaller organizations with significant model risk.
The OCC’s Bulletin 2026-13 states: “Generative AI and agentic AI models are novel and rapidly evolving. As such, they are not within the scope of this guidance.” Do not describe the guidance as a direct generative- or agentic-AI certification. For systems outside its scope, apply broader governance, third-party risk, privacy, security, consumer-protection and operational-resilience practices, and document why those controls fit the use.
Credit unions should also consult the NCUA’s AI resources. International institutions can use the Financial Stability Board’s 2023 third-party risk-management toolkit as a flexible complement to existing standards. Neither resource is a single global rulebook; map expectations to the institution’s charter, regulators, jurisdictions and activity.
A practical approval record
Keep one decision record that lets an independent reviewer reconstruct why the service was approved and under what conditions. Include:
- Use-case scope, impact classification, criticality and accountable executive.
- Data-flow and integration diagrams, affected populations and human-review controls.
- Vendor due-diligence evidence, subcontractor map, financial and resilience assessment, and report or certification scope.
- Model documentation, local test plan, results, segment analysis, limitations and validation conclusion.
- Evidence-gap register, compensating controls, residual-risk acceptance and approval date.
- Contract control matrix, notification and audit rights, incident contacts and exit requirements.
- Monitoring metrics, thresholds, review cadence, escalation owners and records of material changes.
Reapprove or reassess when the use expands, a material model or data change occurs, the supplier’s control environment deteriorates, an incident reveals a new failure mode, or laws and supervisory expectations change.
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Common assessment failures and corrections
“The vendor is reputable, so the risk is low”
Correction: require evidence tied to the exact service, data, population and decision. Reputation can inform, but cannot replace, activity-specific diligence.
“A SOC report proves the AI is accurate”
Correction: inspect the report’s scope and complementary controls, then perform model and workflow testing separately.
“A human reviews every output”
Correction: test whether reviewers receive explanations or source material, have enough time and authority, and actually detect and correct errors.
“The contract says the provider will notify us”
Correction: define materiality, deadlines, required information, cooperation, evidence, remediation and consequences for missed notice.
“The model passed the initial test”
Correction: monitor drift, changes, incidents, segment outcomes and operating conditions; keep a tested fallback and exit plan.
“The 2026 guidance covers our generative or agentic system”
Correction: acknowledge its stated scope limits and apply broader governance and third-party controls appropriate to the system and jurisdiction.
Bottom line for an approval decision
Approve a third-party AI service only when the institution can explain the use, its consequences, the evidence supporting model fitness, the supplier’s ability to operate safely, the information it could not verify, and the controls that reduce remaining risk. Put those obligations in the contract, watch for changes and incidents, and maintain a realistic route to suspend, replace or exit the service. That process preserves institutional accountability while making uncertainty visible and manageable.
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